Masahiro Dogaki

Governing issues and alternate resolutions for a highway transportation agency's transition to asset management

Whether the increasingly poor performance of transportation infrastructure, the budget shortfalls many owners are facing, or the increased demand for more accountability are considered, it is clear that a revolution related to the management of transportation infrastructure is long overdue. There is a broad consensus that this revolution will take shape through adapting and transitioning to the paradigm of asset management (AM). This paper firstly reviews the concept of AM applied to transportation infrastructure, identifies key attributes, and provides a brief overview of the on-going and planned transition to AM by two transportation agencies. Secondly, this paper provides an overview of the related paradigms of performance-based engineering, lifecycle cost analysis, and structural health monitoring, and their role within an integrated AM system. Finally, this paper identifies and discusses relevant issues that transportation infrastructure owners face when they embark on a transition to AM, and proposes an outline for a roadmap to guide this transition.

Optimal Maintenance Planning of Reinforced Concrete Decks on Highway Network

The aim of this paper is to develop an appropriate decision supporting system for planning the rational maintenance scheduling of reinforced concrete (RC) decks, which are one of the structural elements composing steel highway bridges. The paper places emphasis on how to appropriately decide the repair order for some deteriorating RC decks among many existing highway bridges and the paper also takes into account the traffic characteristics of road network and the environmental conditions at the construction sites. The concept of life cycle cost analysis is applied in order to evaluate the fitness of repair method for the damaged RC decks. A Genetic Algorithm (GA) is used to search for a practical and rational combinatorial optimization solution for the maintenance problem because GA has such an advantage that it can provide us with useful solutions for large and complex combinatorial scheduling problems with discontinuous or vague objective functions.